An "elevator pitch" breakdown of areas of applications for Reinforcement Learning & Neural Networks vs. Genetic Algorithms
Your question suggests a confusion of techniques, representations and problems.


Neural Networks are a representation that can be used to approximate functions. A neural network approximates a function that maps from inputs to outputs by optimizing parameters (weights).
Genetic Algorithms are a technique that can be used to optimize a problem. You might chose to use a GA to optimize the weights in a neural network for instance. Or you might use it to optimize a different representation or approximation of a function.
Reinforcement Learning is a problem. In a Reinforcement learning problem, the agent learns a function mapping states to actions. You can learn this function directly in some problem domains, or by a near-direct approximation (like tile-coding), or with a function appropriator (like a neural network).